# model settings
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
    type='EncoderDecoder',
    pretrained=None,
    backbone=dict(type='UniFormer',
                  embed_dim=[64, 128, 320, 512],
                  layers=[3, 4, 8, 3],
                  head_dim=64,
                  mlp_ratio=4.,
                  qkv_bias=True,
                  drop_rate=0.,
                  attn_drop_rate=0.,
                  drop_path_rate=0.1),
    decode_head=dict(type='UPerHead',
                     in_channels=[64, 128, 320, 512],
                     in_index=[0, 1, 2, 3],
                     pool_scales=(1, 2, 3, 6),
                     channels=512,
                     dropout_ratio=0.1,
                     num_classes=19,
                     norm_cfg=norm_cfg,
                     align_corners=False,
                     loss_decode=dict(type='CrossEntropyLoss',
                                      use_sigmoid=False,
                                      loss_weight=1.0)),
    auxiliary_head=dict(type='FCNHead',
                        in_channels=320,
                        in_index=2,
                        channels=256,
                        num_convs=1,
                        concat_input=False,
                        dropout_ratio=0.1,
                        num_classes=19,
                        norm_cfg=norm_cfg,
                        align_corners=False,
                        loss_decode=dict(type='CrossEntropyLoss',
                                         use_sigmoid=False,
                                         loss_weight=0.4)),
    # model training and testing settings
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))
